Various techniques exist that enable Internet-based search engines to receive and process queries from users and to provide search results based thereon. Because these search engines are typically coupled with data stores, such information as queries, search results, and other search data may be conveniently stored for subsequent access. Analysis of this available search data may be interesting to identify trends within patterns of Internet use. However, existing methods for retrieving the search data are ineffective for detecting these trends. Moreover, these existing methods are inappropriate for properly managing the search results to extract meaningful information related to why users are querying an entity (e.g., people, sports teams, cities, and companies) within a particular time frame. Present techniques do not offer sufficient evaluation of the search data to identify and explain adjustments of the popularity of entities. Accordingly, employing a procedure to select peak points of popularity for a particular subject and render queries that explain the peak points would uniquely leverage the query data collected at a search engine and would enhance a user's experience searching the particular subject.